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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# End of week 1 exercise\n",
    "\n",
    "Build a tutor tool by using Ollama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1070317-3ed9-4659-abe3-828943230e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import ollama\n",
    "from IPython.display import Markdown, display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here is the question; type over this to ask something new\n",
    "\n",
    "question = \"\"\"\n",
    "Please explain what this code does and why:\n",
    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer, with streaming\n",
    "\n",
    "\n",
    "messages=[{\"role\":\"user\",\"content\":question}]\n",
    "\n",
    "for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
    "    print(chunk['message']['content'], end='', flush=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f71014-e780-4d3f-a227-1a7c18158a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alternative answer with streaming in Markdown!\n",
    "\n",
    "def stream_response():\n",
    "    messages = [{\"role\": \"user\", \"content\": question}]\n",
    "    \n",
    "    display_markdown = display(Markdown(\"\"), display_id=True)\n",
    "\n",
    "    response_text = \"\"\n",
    "    for chunk in ollama.chat(model=MODEL_LLAMA, messages=messages, stream=True):\n",
    "        \n",
    "        response_text += chunk['message']['content']\n",
    "        clear_output(wait=True)  # Clears previous output\n",
    "        display_markdown.update(Markdown(response_text))  # Updates Markdown dynamically\n",
    "\n",
    "# Run the function\n",
    "stream_response()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c38fdd2a-4b09-402c-ba46-999b22b0cb15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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